To reduce the scan time, one way is to reduce the number of collected signals, that is, to reconstruct the MR image from the undersampled k-space data instead of the full k-space data. However, sampling below the Nyquist rate in the k-space domain causes aliasing artifacts and causes the undersampled image to not be completely restored to a fully-sampled image, that is, MRI reconstruction is an ill-posed problem. To better solve the ill-posed problem, many methods [1]}, [2]}, [3]}, [4]}, [5]} have been proposed to improve the quality of MR images. These methods can be roughly divided into two categories. One is the classic Compressed Sensing-based MRI method (CS-MRI) [1]}, [7]}, [8]}, [9]}, [10]}. In CS-MRI, the sparsity of the MR image in a certain transformed domain (the wavelet domain [8]}, [10]} and total variation [13]}, [2]}) is adopted to reconstruct the aliasing-free image. However, the sparsifying transforms might be too simple to capture complex image details [4]}. Besides, the CS-MRI usually involves iterative optimization, which is time-consuming and leads to loss of details and unwanted artifacts in high undersampling rate [4]}, [5]}. The other is the Deep Learning-based MRI method (DL-MRI), which uses convolution neural network (CNN) to directly reconstruct the aliasing-free image [4]} (Model-free MRI) or learn the parameters of CS-MRI with unrolled architectures [5]}, [20]} (Model-driven MRI). Compared with CS-MRI, DL-MRI tend to have better feature representation ability, faster inference speed, end-to-end trainable paradigm, and significant performance improvement.
